Developing and deploying a use-inspired metapopulation framework for stratified health outcomes
Winter Simulation Conference, 2025
Presenter: Arindam Fadikar
Argonne National Laboratory
2025-12-08
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Roadmap
- Motivation & collaboration context
- MetaRVM modeling framework
- Metapopulation structure
- Disease progression model
- Mixing matrices from a synthetic population
- Implementation as an R package
- Trajectory-oriented optimization
- Case study: influenza in Chicago
Motivation
- Public health decision makers increasingly rely on epidemiological models to:
- Forecast likely futures
- Stress-test interventions
- Plan resource allocation (e.g., beds, staffing)
Motivation
Population-based ODE model
- Aggregate-level dynamics
- Homogeneous mixing assumptions
- Computationally light and fast
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Agent-based model
- Individual-level interactions
- Heterogeneous contact network
- Expensive to run for large population
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Motivation
Population-based ODE model
- Aggregate-level dynamics
- Homogeneous mixing assumptions
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Agent-based model
- Individual-level interactions
- Heterogeneous contact network
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Mixing and force of infection
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- For each stratum, the force of infection depends on:
- Mixing rates between strata \(M = (m_{ij})\)
- Prevalence of infectious individuals in \(j\)th stratum \(I_t^{(j)}\)
Mixing and force of infection
- Effective interacting population (exclude H, D)
\[
\mathrm{MP}_t^{(j)} = P_t^{(j)} - H_t^{(j)} - D_t^{(j)}, \qquad j \in \mathcal{J}.
\]
- Mixing across demographic strata
\(M\): mixing matrix \[
M = (m_{ij}), \qquad \sum_{j \in \mathcal{J}} m_{ij} = 1 \ \forall i,
\]
\[
\mathrm{MP}_{t,\text{eff}}^{(j)} = \sum_{i \in \mathcal{J}} m_{ij}\,\mathrm{MP}_t^{(i)}, \quad
S_{t,\text{eff}}^{(j)} = \sum_{i \in \mathcal{J}} m_{ij}\,S_t^{(i)}, \quad
I_{t,\text{eff}}^{(j)} = \sum_{i \in \mathcal{J}} m_{ij}\,I_t^{(i)}.
\]
Mixing and force of infection
- Force of infection (susceptible vs vaccinated)
\[
\lambda_{s,t}^{(j)} = \beta_s \frac{I_{t,\text{eff}}^{(j)}}{\mathrm{MP}_{t,\text{eff}}^{(j)}},
\qquad
\lambda_{v,t}^{(j)} = \beta_v \frac{I_{t,\text{eff}}^{(j)}}{\mathrm{MP}_{t,\text{eff}}^{(j)}}.
\]
- S → E transition in stratum (j)
\[
p_{\mathrm{SE}}^{(j)} = 1 - \exp\!\big(-\lambda_{s,t}^{(j)} \,\Delta t\big),
\\
\Delta SE_t^{(j)} = p_{\mathrm{SE}}^{(j)}\,S_t^{(j)}.
\]
Age-stratified model - illustration
What we need
- Population counts
- Daily vaccination schedule
- Mixing matrices
- Source: ChiSIM synthetic population
Age-stratified model - illustration
Synthetic population
Statistically representative synthetic population of Chicago
(2.7M people, 1.4M places, 13k activity schedules)
Demographically accurate households from ACS + PUMS at the CBG level
Workplaces generated from County Business Patterns + LEHD OD data
(individuals assigned to realistic work locations)
Schools assigned to all school-aged children
(some adults assigned schools as workplaces)
Additional mixing locations (restaurants, gyms, etc.) from SafeGraph
Age-stratified model - illustration
Mobility –> Mixing
Daytime vs Nightime mixing
Daytime
- Strong location based mixing
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Nighttime
- More household and community mixing
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Vaccination
TOO: overview
- Calibration goal:
- Align model-generated hospitalizations with observed time series
- Approach:
- Choose a set of calibration parameters (e.g., transmission scaling, reporting/observation fraction)
- Define a loss function (e.g., squared error between modeled and observed hospitalizations)
- Use Bayesian optimization to:
- Explore parameter space efficiently
- Balance exploitation and exploration
- Outcome:
- Posterior-like distribution over plausible parameter values
- Uncertainty envelopes around model trajectories
Results
Summary
- MetaRVM provides a use-inspired metapopulation modeling framework for:
- Detailed, stratified tracking of health outcomes
- Infectious disease spread across multiple interacting subpopulations
- Key contributions:
- Uses synthetic population-derived mixing matrices
- Balances realism with computational efficiency
- Delivered as an open-source R package with a Shiny front end
- Case study:
- Demonstrated on influenza-related hospitalizations in Chicago
- Supports prospective surveillance and scenario analysis
Funding acknowledgments
- Chicago Department of Public Health